Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations30116
Missing cells26433
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.4 MiB
Average record size in memory224.1 B

Variable types

DateTime1
Numeric13
Categorical3

Alerts

Bollinger_Lower is highly overall correlated with Bollinger_Middle and 5 other fieldsHigh correlation
Bollinger_Middle is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
Bollinger_Upper is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
MACD is highly overall correlated with MACD_Signal and 1 other fieldsHigh correlation
MACD_Signal is highly overall correlated with MACD and 1 other fieldsHigh correlation
RSI is highly overall correlated with MACD and 1 other fieldsHigh correlation
close is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
high is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
low is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
open is highly overall correlated with Bollinger_Lower and 5 other fieldsHigh correlation
split_ratio is highly imbalanced (99.7%) Imbalance
dividend has 5212 (17.3%) missing values Missing
split_ratio has 21060 (69.9%) missing values Missing
dividend is highly skewed (γ1 = 65.66113002) Skewed
dividend has 24579 (81.6%) zeros Zeros

Reproduction

Analysis started2025-02-16 12:29:21.236957
Analysis finished2025-02-16 12:29:50.579948
Duration29.34 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

date
Date

Distinct2264
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Minimum2015-01-02 00:00:00
Maximum2023-12-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-16T07:29:50.763051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:50.979689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

open
Real number (ℝ)

High correlation 

Distinct17624
Distinct (%)58.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.22712
Minimum0.71249998
Maximum696.28003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.6 KiB
2025-02-16T07:29:51.196675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.71249998
5-th percentile6.5599999
Q136.869999
median68.235001
Q3130.90075
95-th percentile316.43
Maximum696.28003
Range695.56753
Interquartile range (IQR)94.03075

Descriptive statistics

Standard deviation103.07396
Coefficient of variation (CV)1.0182445
Kurtosis5.9616053
Mean101.22712
Median Absolute Deviation (MAD)43.4775
Skewness2.2044201
Sum3048556.1
Variance10624.242
MonotonicityNot monotonic
2025-02-16T07:29:51.434448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 15
 
< 0.1%
11.23999977 14
 
< 0.1%
150 13
 
< 0.1%
40 13
 
< 0.1%
11.30000019 12
 
< 0.1%
55 12
 
< 0.1%
47 12
 
< 0.1%
46.5 11
 
< 0.1%
125 11
 
< 0.1%
118 11
 
< 0.1%
Other values (17614) 29992
99.6%
ValueCountFrequency (%)
0.7124999762 1
< 0.1%
0.7149999738 1
< 0.1%
0.8075000048 2
< 0.1%
0.8125 1
< 0.1%
0.8149999976 1
< 0.1%
0.8299999833 1
< 0.1%
0.8349999785 1
< 0.1%
0.8374999762 1
< 0.1%
0.8424999714 1
< 0.1%
0.8475000262 2
< 0.1%
ValueCountFrequency (%)
696.2800293 1
< 0.1%
687.2199707 1
< 0.1%
681.2600098 1
< 0.1%
676.7399902 1
< 0.1%
676 1
< 0.1%
671.7600098 1
< 0.1%
670.6199951 1
< 0.1%
670.25 1
< 0.1%
669.8200073 1
< 0.1%
668 1
< 0.1%

high
Real number (ℝ)

High correlation 

Distinct18197
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.63361
Minimum0.73500001
Maximum726.20001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.6 KiB
2025-02-16T07:29:51.683502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.73500001
5-th percentile6.6500001
Q137.52
median69.209999
Q3132.53763
95-th percentile320.2625
Maximum726.20001
Range725.46501
Interquartile range (IQR)95.017628

Descriptive statistics

Standard deviation104.64258
Coefficient of variation (CV)1.0195742
Kurtosis5.9995101
Mean102.63361
Median Absolute Deviation (MAD)43.97
Skewness2.2128396
Sum3090913.7
Variance10950.069
MonotonicityNot monotonic
2025-02-16T07:29:51.916619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149.5 13
 
< 0.1%
11.34000015 12
 
< 0.1%
47.5 12
 
< 0.1%
13.14000034 11
 
< 0.1%
11.17000008 11
 
< 0.1%
149 10
 
< 0.1%
71 10
 
< 0.1%
11.60000038 10
 
< 0.1%
11.31000042 10
 
< 0.1%
44 10
 
< 0.1%
Other values (18187) 30007
99.6%
ValueCountFrequency (%)
0.7350000143 1
< 0.1%
0.8000000119 1
< 0.1%
0.8374999762 1
< 0.1%
0.8399999738 1
< 0.1%
0.8525000215 1
< 0.1%
0.8550000191 1
< 0.1%
0.8600000143 1
< 0.1%
0.8650000095 1
< 0.1%
0.8675000072 1
< 0.1%
0.8725000024 1
< 0.1%
ValueCountFrequency (%)
726.2000122 1
< 0.1%
699.539978 1
< 0.1%
699.5 1
< 0.1%
694.8900146 1
< 0.1%
691.3599854 1
< 0.1%
688 1
< 0.1%
684 1
< 0.1%
678.7800293 1
< 0.1%
677.7600098 1
< 0.1%
676.5499878 1
< 0.1%

low
Real number (ℝ)

High correlation 

Distinct18127
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.780755
Minimum0.64249998
Maximum678.90997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.6 KiB
2025-02-16T07:29:52.234094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.64249998
5-th percentile6.43625
Q136.369999
median67.300003
Q3129.485
95-th percentile312.77499
Maximum678.90997
Range678.26747
Interquartile range (IQR)93.114998

Descriptive statistics

Standard deviation101.53677
Coefficient of variation (CV)1.0175987
Kurtosis5.967949
Mean99.780755
Median Absolute Deviation (MAD)42.98
Skewness2.2016045
Sum3004997.2
Variance10309.715
MonotonicityNot monotonic
2025-02-16T07:29:52.529217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 16
 
0.1%
11.06000042 14
 
< 0.1%
11.25 14
 
< 0.1%
11.31000042 12
 
< 0.1%
11 12
 
< 0.1%
44.86000061 11
 
< 0.1%
46.40000153 11
 
< 0.1%
11.14999962 10
 
< 0.1%
11.63000011 10
 
< 0.1%
43.72000122 10
 
< 0.1%
Other values (18117) 29996
99.6%
ValueCountFrequency (%)
0.6424999833 1
< 0.1%
0.7074999809 1
< 0.1%
0.7124999762 1
< 0.1%
0.7749999762 1
< 0.1%
0.7799999714 1
< 0.1%
0.7875000238 1
< 0.1%
0.7950000167 1
< 0.1%
0.7975000143 1
< 0.1%
0.8000000119 1
< 0.1%
0.8025000095 1
< 0.1%
ValueCountFrequency (%)
678.9099731 1
< 0.1%
672.6599731 1
< 0.1%
667.8300171 1
< 0.1%
665.7700195 1
< 0.1%
665.0800171 1
< 0.1%
664.2999878 1
< 0.1%
663 1
< 0.1%
662.8400269 1
< 0.1%
662 1
< 0.1%
661.6699829 1
< 0.1%

close
Real number (ℝ)

High correlation 

Distinct18180
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.21491
Minimum0.69999999
Maximum688.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.6 KiB
2025-02-16T07:29:52.768878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.69999999
5-th percentile6.5481251
Q136.86875
median68.209999
Q3131.04128
95-th percentile316.2225
Maximum688.37
Range687.67
Interquartile range (IQR)94.172533

Descriptive statistics

Standard deviation103.08751
Coefficient of variation (CV)1.0185013
Kurtosis5.9692034
Mean101.21491
Median Absolute Deviation (MAD)43.49
Skewness2.2056129
Sum3048188.1
Variance10627.035
MonotonicityNot monotonic
2025-02-16T07:29:53.023301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.11999989 12
 
< 0.1%
2.279999971 12
 
< 0.1%
11.15999985 11
 
< 0.1%
46.40000153 11
 
< 0.1%
11.43000031 10
 
< 0.1%
46.5 10
 
< 0.1%
11.36999989 10
 
< 0.1%
11.31999969 10
 
< 0.1%
11.75 9
 
< 0.1%
10.89000034 9
 
< 0.1%
Other values (18170) 30012
99.7%
ValueCountFrequency (%)
0.6999999881 1
< 0.1%
0.7124999762 1
< 0.1%
0.7724999785 1
< 0.1%
0.8025000095 1
< 0.1%
0.8125 1
< 0.1%
0.8174999952 1
< 0.1%
0.8299999833 2
< 0.1%
0.8450000286 1
< 0.1%
0.8475000262 1
< 0.1%
0.8525000215 1
< 0.1%
ValueCountFrequency (%)
688.3699951 1
< 0.1%
687.4899902 1
< 0.1%
674.0800171 1
< 0.1%
673.5700073 1
< 0.1%
671.8800049 1
< 0.1%
671.0300293 1
< 0.1%
670.960022 1
< 0.1%
670.6699829 1
< 0.1%
669.8499756 1
< 0.1%
668.3200073 1
< 0.1%

volume
Real number (ℝ)

Distinct26902
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9388977.4
Minimum0
Maximum7.886316 × 108
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size470.6 KiB
2025-02-16T07:29:53.239525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile147380
Q11792300
median3425050
Q36930525
95-th percentile46415700
Maximum7.886316 × 108
Range7.886316 × 108
Interquartile range (IQR)5138225

Descriptive statistics

Standard deviation21949152
Coefficient of variation (CV)2.3377575
Kurtosis189.47948
Mean9388977.4
Median Absolute Deviation (MAD)2079450
Skewness9.2741264
Sum2.8275844 × 1011
Variance4.8176529 × 1014
MonotonicityNot monotonic
2025-02-16T07:29:53.468002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 67
 
0.2%
200 66
 
0.2%
300 31
 
0.1%
500 29
 
0.1%
400 26
 
0.1%
1000 22
 
0.1%
600 22
 
0.1%
700 17
 
0.1%
900 16
 
0.1%
2200 15
 
< 0.1%
Other values (26892) 29805
99.0%
ValueCountFrequency (%)
0 9
 
< 0.1%
100 67
0.2%
200 66
0.2%
300 31
0.1%
400 26
 
0.1%
500 29
0.1%
600 22
 
0.1%
700 17
 
0.1%
800 13
 
< 0.1%
900 16
 
0.1%
ValueCountFrequency (%)
788631600 1
< 0.1%
714352000 1
< 0.1%
711496000 1
< 0.1%
601235200 1
< 0.1%
578006800 1
< 0.1%
374869600 1
< 0.1%
373586800 1
< 0.1%
368776800 1
< 0.1%
332446800 1
< 0.1%
325380000 1
< 0.1%

dividend
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct79
Distinct (%)0.3%
Missing5212
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean0.0083677722
Minimum0
Maximum15.5
Zeros24579
Zeros (%)81.6%
Negative0
Negative (%)0.0%
Memory size470.6 KiB
2025-02-16T07:29:53.673490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15.5
Range15.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.13501613
Coefficient of variation (CV)16.135254
Kurtosis7018.47
Mean0.0083677722
Median Absolute Deviation (MAD)0
Skewness65.66113
Sum208.391
Variance0.018229357
MonotonicityNot monotonic
2025-02-16T07:29:53.861588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 24579
81.6%
2 20
 
0.1%
0.095 13
 
< 0.1%
0.28 12
 
< 0.1%
0.26 12
 
< 0.1%
0.24 8
 
< 0.1%
0.12 8
 
< 0.1%
0.1 8
 
< 0.1%
0.18 7
 
< 0.1%
0.51 7
 
< 0.1%
Other values (69) 230
 
0.8%
(Missing) 5212
 
17.3%
ValueCountFrequency (%)
0 24579
81.6%
0.09 4
 
< 0.1%
0.0925 4
 
< 0.1%
0.095 13
 
< 0.1%
0.1 8
 
< 0.1%
0.11 4
 
< 0.1%
0.115 4
 
< 0.1%
0.12 8
 
< 0.1%
0.13 4
 
< 0.1%
0.132 4
 
< 0.1%
ValueCountFrequency (%)
15.5 1
 
< 0.1%
2.044 1
 
< 0.1%
2 20
0.1%
1.48 4
 
< 0.1%
1.46 2
 
< 0.1%
1.41 4
 
< 0.1%
1.33 2
 
< 0.1%
1.3 4
 
< 0.1%
1.29 1
 
< 0.1%
1.254181 4
 
< 0.1%

Ticker
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.0 MiB
A
2264 
ABBV
2264 
ABT
2264 
ACN
2264 
ADBE
2264 
Other values (9)
18796 

Length

Max length4
Median length3
Mean length3.0978882
Min length1

Characters and Unicode

Total characters93296
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 2264
 
7.5%
ABBV 2264
 
7.5%
ABT 2264
 
7.5%
ACN 2264
 
7.5%
ADBE 2264
 
7.5%
AES 2264
 
7.5%
AFL 2264
 
7.5%
AMC 2264
 
7.5%
AMD 2264
 
7.5%
AOS 2264
 
7.5%
Other values (4) 7476
24.8%

Length

2025-02-16T07:29:54.041212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a 2264
 
7.5%
abbv 2264
 
7.5%
abt 2264
 
7.5%
acn 2264
 
7.5%
adbe 2264
 
7.5%
aes 2264
 
7.5%
afl 2264
 
7.5%
amc 2264
 
7.5%
amd 2264
 
7.5%
aos 2264
 
7.5%
Other values (4) 7476
24.8%

Most occurring characters

ValueCountFrequency (%)
A 24904
26.7%
M 13584
14.6%
B 9056
 
9.7%
E 6792
 
7.3%
C 5212
 
5.6%
K 4528
 
4.9%
S 4528
 
4.9%
D 4528
 
4.9%
N 2948
 
3.2%
O 2948
 
3.2%
Other values (7) 14268
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93296
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 24904
26.7%
M 13584
14.6%
B 9056
 
9.7%
E 6792
 
7.3%
C 5212
 
5.6%
K 4528
 
4.9%
S 4528
 
4.9%
D 4528
 
4.9%
N 2948
 
3.2%
O 2948
 
3.2%
Other values (7) 14268
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93296
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 24904
26.7%
M 13584
14.6%
B 9056
 
9.7%
E 6792
 
7.3%
C 5212
 
5.6%
K 4528
 
4.9%
S 4528
 
4.9%
D 4528
 
4.9%
N 2948
 
3.2%
O 2948
 
3.2%
Other values (7) 14268
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93296
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 24904
26.7%
M 13584
14.6%
B 9056
 
9.7%
E 6792
 
7.3%
C 5212
 
5.6%
K 4528
 
4.9%
S 4528
 
4.9%
D 4528
 
4.9%
N 2948
 
3.2%
O 2948
 
3.2%
Other values (7) 14268
15.3%

split_ratio
Categorical

Imbalance  Missing 

Distinct4
Distinct (%)< 0.1%
Missing21060
Missing (%)69.9%
Memory size2.0 MiB
0.0
9052 
2.0
 
2
0.1
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters27168
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 9052
30.1%
2.0 2
 
< 0.1%
0.1 1
 
< 0.1%
4.0 1
 
< 0.1%
(Missing) 21060
69.9%

Length

2025-02-16T07:29:54.186306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T07:29:54.278591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 9052
> 99.9%
2.0 2
 
< 0.1%
0.1 1
 
< 0.1%
4.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 18108
66.7%
. 9056
33.3%
2 2
 
< 0.1%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27168
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 18108
66.7%
. 9056
33.3%
2 2
 
< 0.1%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27168
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 18108
66.7%
. 9056
33.3%
2 2
 
< 0.1%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27168
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 18108
66.7%
. 9056
33.3%
2 2
 
< 0.1%
1 1
 
< 0.1%
4 1
 
< 0.1%

RSI
Real number (ℝ)

High correlation 

Distinct29900
Distinct (%)99.3%
Missing13
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean51.758341
Minimum5.9879555
Maximum98.452877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.6 KiB
2025-02-16T07:29:54.420995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.9879555
5-th percentile29.714395
Q142.850914
median52.136534
Q361.004979
95-th percentile72.475108
Maximum98.452877
Range92.464921
Interquartile range (IQR)18.154066

Descriptive statistics

Standard deviation13.146475
Coefficient of variation (CV)0.25399723
Kurtosis0.009504711
Mean51.758341
Median Absolute Deviation (MAD)9.0608667
Skewness-0.16111812
Sum1558081.3
Variance172.82981
MonotonicityNot monotonic
2025-02-16T07:29:54.618364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.27757469 3
 
< 0.1%
43.8400307 3
 
< 0.1%
39.65633309 3
 
< 0.1%
68.08326364 2
 
< 0.1%
63.31462921 2
 
< 0.1%
52.33340701 2
 
< 0.1%
41.73972757 2
 
< 0.1%
37.37605093 2
 
< 0.1%
65.03015887 2
 
< 0.1%
26.28597423 2
 
< 0.1%
Other values (29890) 30080
99.9%
(Missing) 13
 
< 0.1%
ValueCountFrequency (%)
5.987955507 1
< 0.1%
5.991088503 1
< 0.1%
5.99148493 1
< 0.1%
5.994874838 1
< 0.1%
6.02486913 1
< 0.1%
6.025151316 1
< 0.1%
6.029478212 1
< 0.1%
6.058037239 1
< 0.1%
6.087921314 1
< 0.1%
6.088633077 1
< 0.1%
ValueCountFrequency (%)
98.45287679 1
< 0.1%
98.33483621 1
< 0.1%
98.00772562 1
< 0.1%
97.3880566 1
< 0.1%
96.60290595 1
< 0.1%
96.57510053 1
< 0.1%
96.34524104 1
< 0.1%
95.84201492 1
< 0.1%
95.38728681 1
< 0.1%
95.21924336 1
< 0.1%

MACD
Real number (ℝ)

High correlation 

Distinct30091
Distinct (%)100.0%
Missing25
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.010532426
Minimum-162.66332
Maximum112.05284
Zeros0
Zeros (%)0.0%
Negative13073
Negative (%)43.4%
Memory size470.6 KiB
2025-02-16T07:29:54.780786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-162.66332
5-th percentile-5.5257823
Q1-0.61168744
median0.13584285
Q31.0090487
95-th percentile4.9864798
Maximum112.05284
Range274.71616
Interquartile range (IQR)1.6207361

Descriptive statistics

Standard deviation6.8416609
Coefficient of variation (CV)649.58073
Kurtosis166.60231
Mean0.010532426
Median Absolute Deviation (MAD)0.81652723
Skewness-3.7927617
Sum316.93123
Variance46.808324
MonotonicityNot monotonic
2025-02-16T07:29:54.962178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.794631956 1
 
< 0.1%
0.9832972665 1
 
< 0.1%
0.2123356814 1
 
< 0.1%
-0.656671483 1
 
< 0.1%
-1.359197978 1
 
< 0.1%
-1.365561331 1
 
< 0.1%
-1.75396606 1
 
< 0.1%
-2.14579984 1
 
< 0.1%
-2.524092086 1
 
< 0.1%
-2.75843487 1
 
< 0.1%
Other values (30081) 30081
99.9%
(Missing) 25
 
0.1%
ValueCountFrequency (%)
-162.6633242 1
< 0.1%
-162.4039144 1
< 0.1%
-161.0160433 1
< 0.1%
-159.6983563 1
< 0.1%
-157.8689595 1
< 0.1%
-153.9079405 1
< 0.1%
-153.6265552 1
< 0.1%
-148.5432294 1
< 0.1%
-144.2806397 1
< 0.1%
-142.8901025 1
< 0.1%
ValueCountFrequency (%)
112.0528362 1
< 0.1%
111.9734616 1
< 0.1%
111.8780409 1
< 0.1%
111.6397333 1
< 0.1%
111.0336205 1
< 0.1%
110.0476072 1
< 0.1%
108.1178289 1
< 0.1%
106.184078 1
< 0.1%
105.2570137 1
< 0.1%
104.8322604 1
< 0.1%

MACD_Signal
Real number (ℝ)

High correlation 

Distinct30083
Distinct (%)100.0%
Missing33
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.010210437
Minimum-142.44375
Maximum104.22788
Zeros0
Zeros (%)0.0%
Negative13180
Negative (%)43.8%
Memory size470.6 KiB
2025-02-16T07:29:55.159062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-142.44375
5-th percentile-5.1243051
Q1-0.57171173
median0.12833167
Q30.96890338
95-th percentile4.8064862
Maximum104.22788
Range246.67163
Interquartile range (IQR)1.5406151

Descriptive statistics

Standard deviation6.4126989
Coefficient of variation (CV)628.05331
Kurtosis149.75956
Mean0.010210437
Median Absolute Deviation (MAD)0.77553053
Skewness-3.5460614
Sum307.16058
Variance41.122707
MonotonicityNot monotonic
2025-02-16T07:29:56.163876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.92785674 1
 
< 0.1%
0.5223502359 1
 
< 0.1%
0.03787267871 1
 
< 0.1%
-0.4854914436 1
 
< 0.1%
-1.057532893 1
 
< 0.1%
-1.567740433 1
 
< 0.1%
-2.012759462 1
 
< 0.1%
-2.351781456 1
 
< 0.1%
-2.599927326 1
 
< 0.1%
-2.908518825 1
 
< 0.1%
Other values (30073) 30073
99.9%
(Missing) 33
 
0.1%
ValueCountFrequency (%)
-142.4437514 1
< 0.1%
-142.3321636 1
< 0.1%
-141.3253576 1
< 0.1%
-140.7793972 1
< 0.1%
-139.1717565 1
< 0.1%
-137.5676076 1
< 0.1%
-136.1650022 1
< 0.1%
-132.4922697 1
< 0.1%
-132.4773208 1
< 0.1%
-128.2523047 1
< 0.1%
ValueCountFrequency (%)
104.2278762 1
< 0.1%
104.0288255 1
< 0.1%
103.7388258 1
< 0.1%
103.1766042 1
< 0.1%
101.9151271 1
< 0.1%
101.4692322 1
< 0.1%
99.40054348 1
< 0.1%
98.66290563 1
< 0.1%
96.73877756 1
< 0.1%
95.73616901 1
< 0.1%

MACD_Diff
Real number (ℝ)

Distinct30083
Distinct (%)100.0%
Missing33
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.00031258173
Minimum-72.484431
Maximum44.314238
Zeros0
Zeros (%)0.0%
Negative14379
Negative (%)47.7%
Memory size470.6 KiB
2025-02-16T07:29:56.339734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-72.484431
5-th percentile-1.5378705
Q1-0.2214228
median0.010670895
Q30.26726528
95-th percentile1.6040314
Maximum44.314238
Range116.79867
Interquartile range (IQR)0.48868808

Descriptive statistics

Standard deviation2.1349055
Coefficient of variation (CV)6829.9114
Kurtosis266.86643
Mean0.00031258173
Median Absolute Deviation (MAD)0.24420515
Skewness-6.1551209
Sum9.4033962
Variance4.5578216
MonotonicityNot monotonic
2025-02-16T07:29:56.565058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.061128814 1
 
< 0.1%
1.937910229 1
 
< 0.1%
2.093456489 1
 
< 0.1%
2.288165798 1
 
< 0.1%
2.04083016 1
 
< 0.1%
1.780076114 1
 
< 0.1%
1.356087979 1
 
< 0.1%
0.9925834787 1
 
< 0.1%
1.234365995 1
 
< 0.1%
1.154552765 1
 
< 0.1%
Other values (30073) 30073
99.9%
(Missing) 33
 
0.1%
ValueCountFrequency (%)
-72.48443096 1
< 0.1%
-70.37571646 1
< 0.1%
-69.46991609 1
< 0.1%
-63.27777352 1
< 0.1%
-60.55139465 1
< 0.1%
-55.25455145 1
< 0.1%
-46.36808768 1
< 0.1%
-39.52733611 1
< 0.1%
-37.30199798 1
< 0.1%
-34.61529147 1
< 0.1%
ValueCountFrequency (%)
44.31423821 1
< 0.1%
41.90176606 1
< 0.1%
41.6872034 1
< 0.1%
40.69551284 1
< 0.1%
39.29787349 1
< 0.1%
33.87969542 1
< 0.1%
32.78462704 1
< 0.1%
31.73635176 1
< 0.1%
31.29846251 1
< 0.1%
27.81258007 1
< 0.1%

Bollinger_Upper
Real number (ℝ)

High correlation 

Distinct30055
Distinct (%)99.9%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean109.58993
Minimum1.0120627
Maximum967.1413
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.6 KiB
2025-02-16T07:29:56.762450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.0120627
5-th percentile7.2411162
Q140.234435
median73.464848
Q3140.36309
95-th percentile342.37281
Maximum967.1413
Range966.12923
Interquartile range (IQR)100.12866

Descriptive statistics

Standard deviation112.78983
Coefficient of variation (CV)1.0291988
Kurtosis6.6383028
Mean109.58993
Median Absolute Deviation (MAD)46.287053
Skewness2.2906468
Sum3298328.3
Variance12721.545
MonotonicityNot monotonic
2025-02-16T07:29:56.948089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.356133043 3
 
< 0.1%
2.224087497 3
 
< 0.1%
42.35735884 2
 
< 0.1%
53.30448114 2
 
< 0.1%
123.5463705 2
 
< 0.1%
2.789899116 2
 
< 0.1%
11.35971891 2
 
< 0.1%
177.6334434 2
 
< 0.1%
20.75580668 2
 
< 0.1%
22.17063186 2
 
< 0.1%
Other values (30045) 30075
99.9%
(Missing) 19
 
0.1%
ValueCountFrequency (%)
1.012062746 1
< 0.1%
1.018998554 1
< 0.1%
1.031165496 1
< 0.1%
1.03399711 1
< 0.1%
1.040650531 1
< 0.1%
1.042906193 1
< 0.1%
1.04441639 1
< 0.1%
1.048809025 1
< 0.1%
1.054293183 1
< 0.1%
1.058112075 1
< 0.1%
ValueCountFrequency (%)
967.1412963 1
< 0.1%
966.6379075 1
< 0.1%
958.2962602 1
< 0.1%
956.3579665 1
< 0.1%
937.6595471 1
< 0.1%
936.4551838 1
< 0.1%
913.5073466 1
< 0.1%
898.6090136 1
< 0.1%
888.9530494 1
< 0.1%
858.7711189 1
< 0.1%

Bollinger_Lower
Real number (ℝ)

High correlation 

Distinct30055
Distinct (%)99.9%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean92.886492
Minimum-347.17276
Maximum645.43874
Zeros0
Zeros (%)0.0%
Negative186
Negative (%)0.6%
Memory size470.6 KiB
2025-02-16T07:29:57.124316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-347.17276
5-th percentile5.0724525
Q133.795974
median63.511805
Q3121.01471
95-th percentile296.05467
Maximum645.43874
Range992.61149
Interquartile range (IQR)87.218736

Descriptive statistics

Standard deviation95.70842
Coefficient of variation (CV)1.0303804
Kurtosis5.9073868
Mean92.886492
Median Absolute Deviation (MAD)41.453745
Skewness2.1101662
Sum2795604.8
Variance9160.1016
MonotonicityNot monotonic
2025-02-16T07:29:57.298399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.235866869 3
 
< 0.1%
1.583912472 3
 
< 0.1%
41.74664125 2
 
< 0.1%
50.83251875 2
 
< 0.1%
116.5856295 2
 
< 0.1%
2.344100947 2
 
< 0.1%
11.03528107 2
 
< 0.1%
149.8665566 2
 
< 0.1%
19.01659322 2
 
< 0.1%
20.74376824 2
 
< 0.1%
Other values (30045) 30075
99.9%
(Missing) 19
 
0.1%
ValueCountFrequency (%)
-347.1727554 1
< 0.1%
-346.6162026 1
< 0.1%
-339.6674225 1
< 0.1%
-337.0391054 1
< 0.1%
-326.1061619 1
< 0.1%
-315.8375977 1
< 0.1%
-305.7841135 1
< 0.1%
-278.8350447 1
< 0.1%
-278.5006198 1
< 0.1%
-246.2353405 1
< 0.1%
ValueCountFrequency (%)
645.4387396 1
< 0.1%
645.3542066 1
< 0.1%
644.1652848 1
< 0.1%
643.8180315 1
< 0.1%
642.2948791 1
< 0.1%
641.7301158 1
< 0.1%
640.8582139 1
< 0.1%
637.1920891 1
< 0.1%
637.153253 1
< 0.1%
635.4628704 1
< 0.1%

Bollinger_Middle
Real number (ℝ)

High correlation 

Distinct29817
Distinct (%)99.1%
Missing19
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean101.23821
Minimum0.9065
Maximum666.77749
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size470.6 KiB
2025-02-16T07:29:57.489742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.9065
5-th percentile6.607875
Q137.0775
median68.543
Q3131.359
95-th percentile315.8714
Maximum666.77749
Range665.87099
Interquartile range (IQR)94.2815

Descriptive statistics

Standard deviation102.50105
Coefficient of variation (CV)1.012474
Kurtosis5.8244661
Mean101.23821
Median Absolute Deviation (MAD)43.487
Skewness2.1828314
Sum3046966.5
Variance10506.466
MonotonicityNot monotonic
2025-02-16T07:29:57.696364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.30249995 3
 
< 0.1%
307.5 3
 
< 0.1%
1.903999984 3
 
< 0.1%
163.675 3
 
< 0.1%
2.295999956 3
 
< 0.1%
11.19349999 3
 
< 0.1%
11.18800001 3
 
< 0.1%
163.775 3
 
< 0.1%
1.800000006 3
 
< 0.1%
55.57999992 2
 
< 0.1%
Other values (29807) 30068
99.8%
(Missing) 19
 
0.1%
ValueCountFrequency (%)
0.9064999998 1
< 0.1%
0.9082499981 1
< 0.1%
0.9113750011 1
< 0.1%
0.9123750001 1
< 0.1%
0.9137500018 1
< 0.1%
0.9150000006 1
< 0.1%
0.9161249995 1
< 0.1%
0.9171250015 1
< 0.1%
0.9206250012 1
< 0.1%
0.9220000029 1
< 0.1%
ValueCountFrequency (%)
666.7774933 1
< 0.1%
665.9424927 1
< 0.1%
665.0919952 1
< 0.1%
663.8999939 1
< 0.1%
663.6094971 1
< 0.1%
661.8659943 1
< 0.1%
661.7529968 1
< 0.1%
661.0629944 1
< 0.1%
660.6119995 1
< 0.1%
660.321994 1
< 0.1%

Target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
1
15677 
0
14439 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters30116
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 15677
52.1%
0 14439
47.9%

Length

2025-02-16T07:29:57.914291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-16T07:29:58.040228image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 15677
52.1%
0 14439
47.9%

Most occurring characters

ValueCountFrequency (%)
1 15677
52.1%
0 14439
47.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30116
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15677
52.1%
0 14439
47.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30116
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15677
52.1%
0 14439
47.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30116
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15677
52.1%
0 14439
47.9%

Interactions

2025-02-16T07:29:47.257957image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:23.207975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:25.154907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:27.050456image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:29.086589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:31.021663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:32.946405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:35.465508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:37.164368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:38.934668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:41.278655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:43.382203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:45.339703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:47.413599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:23.415082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:25.284172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:27.208654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:29.235631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:31.183527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:33.107768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:35.591844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:37.298459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:39.099039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:41.437285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:43.509258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:45.484698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:47.557397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:23.548825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:25.426187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:27.353652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:29.397596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:31.323277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:33.240413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:35.717731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:37.445194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:39.252511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:41.594833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:43.661135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:45.620702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:47.784384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:23.705288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:25.579386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:27.505098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:29.545631image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-16T07:29:37.573182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:39.393787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:41.795426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-02-16T07:29:35.311987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:37.016925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:38.799973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:41.136008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:43.240553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:45.183740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-16T07:29:47.124024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-16T07:29:58.202493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Bollinger_LowerBollinger_MiddleBollinger_UpperMACDMACD_DiffMACD_SignalRSITargetTickerclosedividendhighlowopensplit_ratiovolume
Bollinger_Lower1.0000.9800.9600.160-0.0450.1760.0750.0000.3510.9820.0120.9800.9830.9820.000-0.336
Bollinger_Middle0.9801.0000.9950.151-0.0450.1680.0610.0000.3630.9910.0110.9910.9910.9910.000-0.318
Bollinger_Upper0.9600.9951.0000.144-0.0420.1590.0500.0000.3320.9840.0090.9850.9830.9840.000-0.301
MACD0.1600.1510.1441.0000.2390.9260.7840.0130.1390.2010.0040.1990.2020.2000.000-0.107
MACD_Diff-0.045-0.045-0.0420.2391.000-0.0040.4810.0150.1030.008-0.0080.0070.0070.0050.021-0.007
MACD_Signal0.1760.1680.1590.926-0.0041.0000.6510.0030.1360.2030.0040.2020.2040.2030.000-0.105
RSI0.0750.0610.0500.7840.4810.6511.0000.0000.0850.120-0.0100.1160.1200.1150.029-0.103
Target0.0000.0000.0000.0130.0150.0030.0001.0000.0430.0000.0000.0000.0000.0000.0000.002
Ticker0.3510.3630.3320.1390.1030.1360.0850.0431.0000.3580.0600.3520.3590.3570.0000.181
close0.9820.9910.9840.2010.0080.2030.1200.0000.3581.0000.0101.0001.0001.0000.000-0.319
dividend0.0120.0110.0090.004-0.0080.004-0.0100.0000.0600.0101.0000.0100.0100.0100.0000.002
high0.9800.9910.9850.1990.0070.2020.1160.0000.3521.0000.0101.0000.9991.0000.000-0.314
low0.9830.9910.9830.2020.0070.2040.1200.0000.3591.0000.0100.9991.0001.0000.000-0.323
open0.9820.9910.9840.2000.0050.2030.1150.0000.3571.0000.0101.0001.0001.0000.000-0.319
split_ratio0.0000.0000.0000.0000.0210.0000.0290.0000.0000.0000.0000.0000.0000.0001.0000.000
volume-0.336-0.318-0.301-0.107-0.007-0.105-0.1030.0020.181-0.3190.002-0.314-0.323-0.3190.0001.000

Missing values

2025-02-16T07:29:49.691767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-16T07:29:49.962167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-16T07:29:50.345793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

dateopenhighlowclosevolumedividendTickersplit_ratioRSIMACDMACD_SignalMACD_DiffBollinger_UpperBollinger_LowerBollinger_MiddleTarget
02015-01-0241.18000041.31000140.36999940.56000115292000.1ANaNNaNNaNNaNNaNNaNNaNNaN0
12015-01-0540.32000040.45999939.70000139.79999920418000.0ANaNNaNNaNNaNNaNNaNNaNNaN0
22015-01-0639.81000140.02000039.02000039.18000020806000.0ANaNNaNNaNNaNNaNNaNNaNNaN1
32015-01-0739.52000039.81000139.29000139.70000133597000.0ANaNNaNNaNNaNNaNNaNNaNNaN1
42015-01-0840.24000240.98000040.18000040.88999921163000.0ANaNNaNNaNNaNNaNNaNNaNNaN0
52015-01-0941.00000041.00000040.29000140.59000016449000.0ANaNNaNNaNNaNNaNNaNNaNNaN0
62015-01-1240.61000140.72000139.95000140.11000127708000.0ANaNNaNNaNNaNNaNNaNNaNNaN0
72015-01-1340.47000140.70000139.33000239.54999920131000.0ANaNNaNNaNNaNNaNNaNNaNNaN0
82015-01-1439.02999939.09999838.20999939.06000151340000.0ANaNNaNNaNNaNNaNNaNNaNNaN0
92015-01-1539.33000239.41000037.99000238.00999826289000.0ANaNNaNNaNNaNNaNNaNNaNNaN1
dateopenhighlowclosevolumedividendTickersplit_ratioRSIMACDMACD_SignalMACD_DiffBollinger_UpperBollinger_LowerBollinger_MiddleTarget
301062023-12-1588.92140289.90802888.59532289.448158132555070.0MMMNaN75.8578342.6222502.2258440.39640690.48562577.33210383.9088640
301072023-12-1889.46488289.71572188.34448288.52006536167040.0MMMNaN70.2481602.6137322.3034210.31031090.92287577.77528884.3490811
301082023-12-1988.93812689.25585288.53678988.83779131511010.0MMMNaN71.0377742.6026172.3632600.23935691.26127178.37919984.8202350
301092023-12-2088.46154088.85451586.64715686.68060338838900.0MMMNaN59.4929622.3921662.3690420.02312491.03467879.41515785.2249171
301102023-12-2187.14046588.31939787.04849288.26087226242630.0MMMNaN64.0962202.3260832.360450-0.03436791.02267280.27916985.6509201
301112023-12-2288.52842789.80769388.41973188.90467827289130.0MMMNaN65.8013322.2991582.348192-0.04903391.01775481.15197986.0848671
301122023-12-2688.87960190.79431288.85451590.39297533321760.0MMMNaN69.4171732.3705872.3526710.01791691.27364081.81917086.5464051
301132023-12-2790.30100391.22073490.03344790.91973129227850.0MMMNaN70.6019162.4415542.3704470.07110791.65868782.28947586.9740811
301142023-12-2890.85284492.12374990.84448291.71405033602820.0MMMNaN72.3418762.5326962.4028970.12979992.05297982.83331087.4431440
301152023-12-2991.55518391.93979690.92809391.40467828876220.0MMMNaN70.5894862.5505622.4324300.11813292.27697383.46633887.8716560